Abstract
Databricks is an analytics service based on the Apache Spark open source project. Apache Spark is a batch processing and real time processing environment. Apache Spark is quite popular among data scientists because of its ability to analyze huge amounts of data, its streaming capabilities, graph computation, machine learning, and interactive queries engine. Spark provides in-memory cluster computing. One of the popular tools for big data analytics on Spark is Databricks. Databricks has been used for ingesting a significant amount of data, cleaning data, applying machine learning, and so forth. In February 2018, there was an integration between Microsoft Azure and Databricks that provides a better collaboration between data engineers, data scientists, and data analytics. This integration provides data science and data engineering teams with a fast, easy, and collaborative Spark-based platform in Azure [1]. Azure Databricks is a new platform for big data analytics and machine learning. The notebook in Azure Databricks enables data engineers, data scientists, and business analysts to collaborate using a single tool. This chapter gives an overview of what Azure Databricks is, the environment it inhabits, and its use in data science.
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